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Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography

Author

Listed:
  • Dariusz Majerek

    (Faculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, Poland)

  • Tomasz Rymarczyk

    (Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland
    Research & Development Center Netrix S.A., 20-704 Lublin, Poland)

  • Dariusz Wójcik

    (Research & Development Center Netrix S.A., 20-704 Lublin, Poland)

  • Edward Kozłowski

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Magda Rzemieniak

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Janusz Gudowski

    (Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland)

  • Konrad Gauda

    (Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland)

Abstract

This paper describes the method developed using the Extreme Gradient Boosting (Xgboost) algorithm that allows high-resolution imaging using the ultrasound tomography (UST) signal. More precisely, we can locate, isolate, and use the reflective peaks from the UST signal to achieve high-resolution images with low noise, which are far more useful for the location of points where the reflection occurred inside the experimental tank. Each reconstruction is divided into two parts, estimation of starting points of wave packets of raw signal (SAT—starting arrival time) and image reconstruction via XGBoost algorithm based on SAT matrix. This technology is the basis of a project to design non-invasive monitoring and diagnostics of technological processes. In this paper, we present a method of the complete solution for monitoring industrial processes. The measurements used in the study were obtained with the author’s solution of ultrasound tomography.

Suggested Citation

  • Dariusz Majerek & Tomasz Rymarczyk & Dariusz Wójcik & Edward Kozłowski & Magda Rzemieniak & Janusz Gudowski & Konrad Gauda, 2021. "Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography," Energies, MDPI, vol. 14(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7549-:d:677316
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    References listed on IDEAS

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    1. Tomasz Rymarczyk & Grzegorz Kłosowski & Anna Hoła & Jan Sikora & Tomasz Wołowiec & Paweł Tchórzewski & Stanisław Skowron, 2021. "Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls," Energies, MDPI, vol. 14(10), pages 1-22, May.
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    Cited by:

    1. Bartosz Przysucha & Dariusz Wójcik & Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Marcin Gąsior, 2023. "Analysis of Reconstruction Energy Efficiency in EIT and ECT 3D Tomography Based on Elastic Net," Energies, MDPI, vol. 16(3), pages 1-22, February.
    2. Dariusz Wójcik & Tomasz Rymarczyk & Bartosz Przysucha & Michał Gołąbek & Dariusz Majerek & Tomasz Warowny & Manuchehr Soleimani, 2023. "Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography," Energies, MDPI, vol. 16(3), pages 1-14, January.

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